An e-nose made of carbon nanotube based quantum resistive sensors for the detection of eighteen polar/nonpolar VOC biomarkers of lung cancer

Nose-on-Chip Nanobiosensors for Early Detection of Lung Cancer Breath Biomarkers

Lung cancer remains a global health concern, demanding the development of noninvasive, prompt, selective, and point-of-care diagnostic tools. Correspondingly, breath analysis using nanobiosensors has emerged as a promising noninvasive nose-on-chip technique for the early detection of lung cancer through monitoring diversified biomarkers such as volatile organic compounds/gases in exhaled breath. This comprehensive review summarizes the state-of-the-art breath-based lung cancer diagnosis employing chemiresistive-module nanobiosensors supported by theoretical findings. It unveils the fundamental mechanisms and biological basis of breath biomarker generation associated with lung cancer, technological advancements, and clinical implementation of nanobiosensor-based breath analysis. It explores the merits, challenges, and potential alternate solutions in implementing these nanobiosensors in clinical settings, including standardization, biocompatibility/toxicity analysis, green and sustainable technologies, life-cycle assessment, and scheming regulatory modalities. It highlights nanobiosensors' role in facilitating precise, real-time, and on-site detection of lung cancer through breath analysis, leading to improved patient outcomes, enhanced clinical management, and remote personalized monitoring. Additionally, integrating these biosensors with artificial intelligence, machine learning, Internet-of-things, bioinformatics, and omics technologies is discussed, providing insights into the prospects of intelligent nose-on-chip lung cancer sniffing nanobiosensors. Overall, this review consolidates knowledge on breathomic biosensor-based lung cancer screening, shedding light on its significance and potential applications in advancing state-of-the-art medical diagnostics to reduce the burden on hospitals and save human lives.

Application of electronic nose and machine learning used to detect soybean gases under water stress and variability throughout the daytime

The development of non-invasive methods and accessible tools for application to plant phenotyping is considered a breakthrough. This work presents the preliminary results using an electronic nose (E-Nose) and machine learning (ML) as affordable tools. An E-Nose is an electronic system used for smell global analysis, which emulates the human nose structure. The soybean (Glycine Max) was used to conduct this experiment under water stress. Commercial E-Nose was used, and a chamber was designed and built to conduct the measurement of the gas sample from the soybean. This experiment was conducted for 22 days, observing the stages of plant growth during this period. This chamber is embedded with relative humidity [RH (%)], temperature (°C), and CO2 concentration (ppm) sensors, as well as the natural light intensity, which was monitored. These systems allowed intermittent monitoring of each parameter to create a database. The soil used was the red-yellow dystrophic type and was covered to avoid evapotranspiration effects. The measurement with the electronic nose was done daily, during the morning and afternoon, and in two phenological situations of the plant (with the healthful soy irrigated with deionized water and underwater stress) until the growth V5 stage to obtain the plant gases emissions. Data mining techniques were used, through the software "Weka™" and the decision tree strategy. From the evaluation of the sensors database, a dynamic variation of plant respiration pattern was observed, with the two distinct behaviors observed in the morning (~9:30 am) and afternoon (3:30 pm). With the initial results obtained with the E-Nose signals and ML, it was possible to distinguish the two situations, i.e., the irrigated plant standard and underwater stress, the influence of the two periods of daylight, and influence of temporal variability of the weather. As a result of this investigation, a classifier was developed that, through a non-invasive analysis of gas samples, can accurately determine the absence of water in soybean plants with a rate of 94.4% accuracy. Future investigations should be carried out under controlled conditions that enable early detection of the stress level.

Au- and Pd-Doped SnS2 Monolayers for Lung Cancer Biomarkers (C3H6O, C6H6, and C5H8) Detection: A Density Functional Theory Investigation

An efficient and noninvasive method of sensing lung cancer at an early stage is through detecting its biomarkers in the patient's exhaled breath. Acetone (C3H6O), benzene (C6H6), and isoprene (C5H8) emerged as crucial biomarkers, which were significantly elevated in lung cancer patients. Here, we investigated the adsorption behaviors of the three gas molecules on pristine and transition metal (TM)-doped (Au and Pd) SnS2 monolayers using the density functional theory (DFT) method. Our findings indicate that both Au- and Pd-doped SnS2 display higher adsorption energies (-0.53 to -1.313 eV) than that of the pure SnS2 monolayer (0.031 to 0.066 eV). Specifically, Pd-SnS2 exhibits smaller adsorption energy compared to that of Au-SnS2 when capturing C3H6O, C6H6, and C5H8. The estimated recovery times for Pd-SnS2 (8.016 × 10-4 to 16.02 s) are shorter compared to those of Au-SnS2 (1.11 to 1.14 × 1010 s), indicating the superior capability of Pd-SnS2 over Au-SnS2 as a reversible sensor. Afterward, calculations of band structure, projected density of states (PDOS), and charge transfer were performed, which further substantiates the more promising potentials for Pd-doped SnS2 monolayer as gas sensors over the others. Overall, our results suggest that Pd-SnS2 is a better candidate for C3H6O, C6H6, and C5H8 detection over Au-SnS2 and pristine SnS2.

The State of the Art on Graphene-Based Sensors for Human Health Monitoring through Breath Biomarkers

The field of organic-borne biomarkers has been gaining relevance due to its suitability for diagnosing pathologies and health conditions in a rapid, accurate, non-invasive, painless and low-cost way. Due to the lack of analytical techniques with features capable of analysing such a complex matrix as the human breath, the academic community has focused on developing electronic noses based on arrays of gas sensors. These sensors are assembled considering the excitability, sensitivity and sensing capacities of a specific nanocomposite, graphene. In this way, graphene-based sensors can be employed for a vast range of applications that vary from environmental to medical applications. This review work aims to gather the most relevant published papers under the scope of "Graphene sensors" and "Biomarkers" in order to assess the state of the art in the field of graphene sensors for the purposes of biomarker identification. During the bibliographic search, a total of six pathologies were identified as the focus of the work. They were lung cancer, gastric cancer, chronic kidney diseases, respiratory diseases that involve inflammatory processes of the airways, like asthma and chronic obstructive pulmonary disease, sleep apnoea and diabetes. The achieved results, current development of the sensing sensors, and main limitations or challenges of the field of graphene sensors are discussed throughout the paper, as well as the features of the experiments addressed.

Diagnosis of Carcinogenic Pathologies through Breath Biomarkers: Present and Future Trends

The assessment of volatile breath biomarkers has been targeted with a lot of interest by the scientific and medical communities during the past decades due to their suitability for an accurate, painless, non-invasive, and rapid diagnosis of health states and pathological conditions. This paper reviews the most relevant bibliographic sources aiming to gather the most pertinent volatile organic compounds (VOCs) already identified as putative cancer biomarkers. Here, a total of 265 VOCs and the respective bibliographic sources are addressed regarding their scientifically proven suitability to diagnose a total of six carcinogenic diseases, namely lung, breast, gastric, colorectal, prostate, and squamous cell (oesophageal and laryngeal) cancers. In addition, future trends in the identification of five other forms of cancer, such as bladder, liver, ovarian, pancreatic, and thyroid cancer, through perspective volatile breath biomarkers are equally presented and discussed. All the results already achieved in the detection, identification, and quantification of endogenous metabolites produced by all kinds of normal and abnormal processes in the human body denote a promising and auspicious future for this alternative diagnostic tool, whose future passes by the development and employment of newer and more accurate collection and analysis techniques, and the certification for utilisation in real clinical scenarios.

Machine-Learning-Aided NO2 Discrimination with an Array of Graphene Chemiresistors Covalently Functionalized by Diazonium Chemistry

Boosted by the emerging need for highly integrated gas sensors in the internet of things (IoT) ecosystems, electronic noses (e-noses) are gaining interest for the detection of specific molecules over a background of interfering gases. The sensing of nitrogen dioxide is particularly relevant for applications in environmental monitoring and precision medicine. Here we present an easy and efficient functionalization procedure to covalently modify graphene layers, taking advantage of diazonium chemistry. Separate graphene layers were functionalized with one of three different aryl rings: 4-nitrophenyl, 4-carboxyphenyl and 4-bromophenyl. The distinct modified graphene layers were assembled with a pristine layer into an e-nose for NO2 discrimination. A remarkable sensitivity to NO2 was demonstrated through exposure to gaseous solutions with NO2 concentrations in the 1-10 ppm range at room temperature. Then, the discrimination capability of the sensor array was tested by carrying out exposure to several interfering gases and analyzing the data through multivariate statistical analysis. This analysis showed that the e-nose can discriminate NO2 among all the interfering gases in a two-dimensional principal component analysis space. Finally, the e-nose was trained to accurately recognize NO2 contributions with a linear discriminant analysis approach, thus providing a metric for discrimination assessment with a prediction accuracy above 95 %.

Research progress of electronic nose technology in exhaled breath disease analysis

Exhaled breath analysis has attracted considerable attention as a noninvasive and portable health diagnosis method due to numerous advantages, such as convenience, safety, simplicity, and avoidance of discomfort. Based on many studies, exhaled breath analysis is a promising medical detection technology capable of diagnosing different diseases by analyzing the concentration, type and other characteristics of specific gases. In the existing gas analysis technology, the electronic nose (eNose) analysis method has great advantages of high sensitivity, rapid response, real-time monitoring, ease of use and portability. Herein, this review is intended to provide an overview of the application of human exhaled breath components in disease diagnosis, existing breath testing technologies and the development and research status of electronic nose technology. In the electronic nose technology section, the three aspects of sensors, algorithms and existing systems are summarized in detail. Moreover, the related challenges and limitations involved in the abovementioned technologies are also discussed. Finally, the conclusion and perspective of eNose technology are presented.

Alveolar gradients in breath analysis. A pilot study with comparison of room air and inhaled air by simultaneous measurements using ion mobility spectrometry

Analyzing exhaled breath samples, especially using a highly sensitive method such as MCC/IMS (multi-capillary column/ion mobility spectrometry), may also detect analytes that are derived from exogenous production. In this regard, there is a discussion about the optimal interpretation of exhaled breath, either by considering volatile organic compounds (VOCs) only in exhaled breath or by additionally considering the composition of room air and calculating the alveolar gradients. However, there are no data on whether the composition and concentration of VOCs in room air are identical to those in truly inhaled air directly before analyzing the exhaled breath. The current study aimed to determine whether the VOCs in room air, which are usually used for the calculation of alveolar gradients, are identical to the VOCs in truly inhaled air. For the measurement of inhaled air and room air, two IMS, each coupled with an MCC that provided a pre-separation of the VOCs, were used in parallel. One device was used for sampling room air and the other for sampling inhaled air. Each device was coupled with a newly invented system that cleaned room air and provided a clean carrier gas, whereas formerly synthetic air had to be used as a carrier gas. In this pilot study, a healthy volunteer underwent three subsequent runs of sampling of inhaled air and simultaneous sampling and analysis of room air. Three of the selected 11 peaks (P4-unknown, P5-1-Butanol, and P9-Furan, 2-methyl-) had significantly higher intensities during inspiration than in room air, and four peaks (P1-1-Propanamine, N-(phenylmethylene), P2-2-Nonanone, P3-Benzene, 1,2,4-trimethyl-, and P11-Acetyl valeryl) had higher intensities in room air. Furthermore, four peaks (P6-Benzaldehyde, P7-Pentane, 2-methyl-, P8-Acetone, and P10-2-Propanamine) showed inconsistent differences in peak intensities between inhaled air and room air. To the best of our knowledge, this is the first study to compare simultaneous sampling of room air and inhaled air using MCC/IMS. The simultaneous measurement of inhaled air and room air showed that using room air for the calculation of alveolar gradients in breath analysis resulted in different alveolar gradient values than those obtained by measuring truly inhaled air.

CuO-ZnO p-n junctions for accurate prediction of multiple volatile organic compounds aided by machine learning algorithms

Detection and quantification of multiple volatile organic compounds (VOCs) are emerging as critical requirements for several niche applications including healthcare. It is desirable to get multiple gases identified rapidly and using minimum number of sensors. Heterojunctions of metal oxides are still among the top-picks for efficient VOC sensing because they unfold exciting sensing characteristics in addition to enhanced response. This work reports the synthesis of nanostructures of CuO, ZnO, and three CuO-ZnO p-n junctions having different weight percentages (1-0.5, 1-1, and 0.5-1) of CuO and ZnO, using a facile one-pot hydrothermal method. The nanomaterials were characterized using X-ray diffraction, field emission scanning electron microscopy, and UV-Visible spectroscopy. Resistive sensors were fabricated of all five nanomaterials and were tested for 25-200 ppm of four VOCs - isopropanol, methanol, acetonitrile, and toluene. The CuO and CuO-ZnO (1-0.5) sensors showed the highest response for isopropanol (7.5-65.3% and 19-122%, respectively) at 250 °C, CuO-ZnO (1-1) and CuO-ZnO (0.5-1) exhibited the highest responses for methanol (9-60%) and isopropanol (15-120%), respectively at 350 °C, and the intrinsic ZnO showed maximum response to toluene (29-76%) at 400 °C. All the sensing layers were observed to exhibit finite responses to the other three VOCs so, an attempt to classify and quantify the four VOCs accurately was made using support vector machine (SVM) and multiple linear regression (MLR) algorithms. The response and response times of two sensors were observed to be sufficient as inputs to the machine learning algorithms for classifying and quantifying all the four VOCs. The combinations of (CuO-ZnO (1-0.5) & (1-1) and CuO-ZnO (1-1) & (0.5-1) demonstrated the highest classification accuracy of 98.13% with SVM. The combination of CuO-ZnO (1-0.5) & (1-1) demonstrated the best quantification of the four VOCs using MLR.

A metal-organic framework loaded paper-based chemiluminescence sensor for trace acetone detection in exhaled breath

Trace acetone determination in breath can be regarded as a noninvasive method for diagnosis of diabetes. Here, a paper-based CL gas sensor combined with UiO-66 as the preconcentrator was established for sensitive detection of trace acetone in exhaled breath. UiO-66 with excellent adsorption performance and unique water stability was used for the adsorption and enrichment of acetone gas under high humidity conditions in exhaled breath. As acetone can remarkably increase the chemiluminescence (CL) of the 2,4-dinitrophenylhydrazine (DNPH)-potassium permanganate (KMnO₄) system, a sensitive CL device based on a paper substrate for trace acetone detection was established and the detection limit was 0.03 ppm. The fabricated method was used to assess the content of trace acetone in exhaled breath with satisfactory recoveries of 90-110%. It is expected to realize the noninvasive determination of acetone for diabetic patients in exhaled breath.

SnO2/rGO nanocomposite for the detection of biomarkers of lung cancer

Metal oxide-based sensors have been widely used to detection biomarkers in exhaled breath for identification of various diseases such as asthma, diabetes, halitosis, and lung cancer. Herein, we proposed one step hydrothermal method for the preparation of SnO2 nanospheres and reduced graphene oxide incorporated SnO2 nanospheres for the detection of two important biomarkers such as decane and heptane from the exhaled breath of lung cancer patients. The as prepared materials are investigated in detail through various analytical techniques and the findings are consistent with each other. The sensing response of the proposed sensors were systematically investigated to enhance their sensing performance as a function of operating temperatures and gas concentration, and different analyte gases. The sensors showed maximum sensing response toward heptane and decane compared to other interfering gases such as hydrogen, carbon monoxide, acetone, ethanol, and methanol at 125 °C. The proposed sensors exhibit excellent detection range as low as 1 ppm with appreciably fast response and recovery time. Lung cancer patients may be easily screened using the proposed sensor, by detecting decane and heptane in their exhaled breath.

Trends in the Development of Electronic Noses Based on Carbon Nanotubes Chemiresistors for Breathomics

The remarkable potential of breath analysis in medical care and diagnosis, and the consequent development of electronic noses, is currently attracting the interest of the research community. This is mainly due to the possibility of applying the technique for early diagnosis, screening campaigns, or tracking the effectiveness of treatment. Carbon nanotubes (CNTs) are known to be good candidates for gas sensing, and they have been recently considered for the development of electronic noses. The present work has the aim of reviewing the available literature on the development of CNTs-based electronic noses for breath analysis applications, detailing the functionalization procedure used to prepare the sensors, the breath sampling techniques, the statistical analysis methods, the diseases under investigation, and the population studied. The review is divided in two main sections: one focusing on the e-noses completely based on CNTs and one reporting on the e-noses that feature sensors based on CNTs, along with sensors based on other materials. Finally, a classification is presented among studies that report on the e-nose capability to discriminate biomarkers, simulated breath, and animal or human breath.

Exhaled VOCs can discriminate subjects with COVID-19 from healthy controls

COVID-19 detection currently relies on testing by reverse transcription polymerase chain reaction (RT-PCR) or antigen testing. However, SARS-CoV-2 is expected to cause significant metabolic changes in infected subjects due to both metabolic requirements for rapid viral replication and host immune responses. Analysis of volatile organic compounds (VOCs) from human breath can detect these metabolic changes and is therefore an alternative to RT-PCR or antigen assays. To identify VOC biomarkers of COVID-19, exhaled breath samples were collected from two sample groups into Tedlar bags: negative COVID-19 (n= 12) and positive COVID-19 symptomatic (n= 14). Next, VOCs were analyzed by headspace solid phase microextraction coupled to gas chromatography-mass spectrometry. Subjects with COVID-19 displayed a larger number of VOCs as well as overall higher total concentration of VOCs (p< 0.05). Univariate analyses of qualified endogenous VOCs showed approximately 18% of the VOCs were significantly differentially expressed between the two classes (p< 0.05), with most VOCs upregulated. Machine learning multivariate classification algorithms distinguished COVID-19 subjects with over 95% accuracy. The COVID-19 positive subjects could be differentiated into two distinct subgroups by machine learning classification, but these did not correspond with significant differences in number of symptoms. Next, samples were collected from subjects who had previously donated breath bags while experiencing COVID-19, and subsequently recovered (COVID Recovered subjects (n= 11)). Univariate and multivariate results showed >90% accuracy at identifying these new samples as Control (COVID-19 negative), thereby validating the classification model and demonstrating VOCs dysregulated by COVID are restored to baseline levels upon recovery.

Mechanistic Approach for Long-Term Stability of a Polyethylene Glycol-Carbon Black Nanocomposite Sensor

Polymer-carbon nanocomposite sensor is a promising molecular sensing device for electronic nose (e-nose) due to its printability, variety of polymer materials, and low operation temperature; however, the lack of stability in an air environment has been an inevitable issue. Here, we demonstrate a design concept for realizing long-term stability in a polyethylene glycol (PEG)-carbon black (CB) nanocomposite sensor by understanding the underlying phenomena that cause sensor degradation. Comparison of the sensing properties and infrared spectroscopy on the same device revealed that the oxidation-induced consumption of PEG is a crucial factor for the sensor degradation. According to the mechanism, we introduced an antioxidizing agent (i.e., ascorbic acid) into the PEG-CB nanocomposite sensor to suppress the PEG oxidation and successfully demonstrated the long-term stability of sensing properties under an air environment for 30 days, which had been difficult in conventional polymer-carbon nanocomposite sensors.

Odors and cancer: Current status and future directions

Cancer is the second leading cause of death in the world. Because tumors detected at early stages are easier to treat, the search for biomarkers-especially non-invasive ones-that allow early detection of malignancies remains a central goal to reduce cancer mortality. Cancer, like other pathologies, often alters body odors, and much has been done by scientists over the last few decades to assess the value of volatile organic compounds (VOCs) as signatures of cancers. We present here a quantitative review of 208 studies carried out between 1984 and 2020 that explore VOCs as potential biomarkers of cancers. We analyzed the main findings of these studies, listing and classifying VOCs related to different cancer types while considering both sampling methods and analysis techniques. Considering this synthesis, we discuss several of the challenges and the most promising prospects of this research direction in the war against cancer.

Odor-Based Nanomechanical Discrimination of Fuel Oils Using a Single Type of Designed Nanoparticles with Nonlinear Viscoelasticity

Odors are one of the most diverse and complicated gaseous mixtures so that their discrimination is challenging yet attractive because of the rich information about their origin. The more similar the properties of odors are, the more difficult the discrimination becomes. The practical applications, however, often demand such discrimination, especially with a compact sensing platform. In this paper, we show that a nanomaterial designed for a specific type of odors can clearly discriminate them even with a single nanomechanical sensing channel. Fuel oils and their mixture are used as a model target that has similar chemical properties but different compositions mainly consisting of paraffinic, olefinic, naphthenic, and aromatic hydrocarbons. We demonstrate using octadecyl functionalized silica-titania nanoparticles that the difference in the compositions is successfully picked up based on their high affinity for the aliphatic hydrocarbons and alkyl chain length dependent nonlinear viscoelastic behavior. Such a properly designed material is proved to derive sufficient information from a series of analytes to discriminate them even with a single sensing element. This approach provides a guideline to prepare various sensors whose response properties are distinct and optimized depending on applications.

Exploring the performance of a functionalized CNT-based sensor array for breathomics through clustering and classification algorithms: from gas sensing of selective biomarkers to discrimination of chronic obstructive pulmonary disease

An array of carbon nanotube (CNT)-based sensors was produced for sensing selective biomarkers and evaluating breathomics applications with the aid of clustering and classification algorithms. We assessed the sensor array performance in identifying target volatiles and we explored the combination of various classification algorithms to analyse the results obtained from a limited dataset of exhaled breath samples. The sensor array was exposed to ammonia (NH₃), nitrogen dioxide (NO₂), hydrogen sulphide (H₂S), and benzene (C₆H₆). Among them, ammonia (NH₃) and nitrogen dioxide (NO₂) are known biomarkers of chronic obstructive pulmonary disease (COPD). Calibration curves for individual sensors in the array were obtained following exposure to the four target molecules. A remarkable response to ammonia (NH₃) and nitrogen dioxide (NO₂), according to benchmarking with available data in the literature, was observed. Sensor array responses were analyzed through principal component analysis (PCA), thus assessing the array selectivity and its capability to discriminate the four different target volatile molecules. The sensor array was then exposed to exhaled breath samples from patients affected by COPD and healthy control volunteers. A combination of PCA, supported vector machine (SVM), and linear discrimination analysis (LDA) shows that the sensor array can be trained to accurately discriminate healthy from COPD subjects, in spite of the limited dataset.

Extensive application of clustering and classification algorithms shows the potential of a CNT-based sensor array in breathomics.

Sensing Behavior of Metal-Free Porphyrin and Zinc Phthalocyanine Thin Film towards Xylene-Styrene and HCl Vapors in Planar Optical Waveguide

The sensing behavior of a thin film composed of metal-free 5, 10, 15, 20-tetrakis (p-hydroxy phenyl) porphyrin and zinc phthalocyanine complex towards m-xylene, styrene, and HCl vapors in a homemade planar optical waveguide (POWG), was studied at room temperature. The thin film was deposited on the surface of potassium ion-exchanged glass substrate, using vacuum spin-coating method, and a semiconductor laser light (532 nm) as the guiding light. Opto-chemical changes of the film exposing with hydrochloric gas, m-xylene, and styrene vapor, were analyzed firstly with UV-Vis spectroscopy. The fabricated POWG shows good correlation between gas exposure response and absorbance change within the gas concentration range 10-1500 ppm. The limit of detection calculated from the logarithmic calibration curve was proved to be 11.47, 21.08, and 14.07 ppm, for HCl gas, m-xylene, and styrene vapors, respectively. It is interesting to find that the film can be recovered to the initial state with trimethylamine vapors after m-xylene, styrene exposures as well as HCl exposure. The gas-film interaction mechanism was discussed considering protonation and π-π stacking with planar aromatic analyte molecules.

Enhancing mixed toluene and formaldehyde pollutant removal by Zamioculcas zamiifolia combined with Sansevieria trifasciata and its CO2 emission

Indoor air pollutants comprise both polar and non-polar volatile organic compounds (VOCs). Indoor potted plants are well known for their innate ability to improve indoor air quality (IAQ) by detoxification of indoor air pollutants. In this study, a combination of two different plant species comprising a C3 plant (Zamioculcas zamiifolia) and a crassulacean acid metabolism (CAM) plant (Sansevieria trifasciata) was used to remove polar and non-polar VOCs and minimize CO₂ emission from the chamber. Z. zamiifolia and S. trifasciata, when combined, were able to remove more than 95% of pollutants within 48 h and could do so for six consecutive pollutant's exposure cycles. The CO₂ concentration was reduced from 410 down to 160 ppm inside the chamber. Our results showed that using plant growth medium rather than soil had a positive effect on decreasing CO₂. We also re-affirmed the role of formaldehyde dehydrogenase in the detoxification and metabolism of formaldehyde and that exposure of plants to pollutants enhances the activity of this enzyme in the shoots of both Z. zamiifolia and S. trifasciata. Overall, a mixed plant of Z. zamiifolia and S. trifasciata was more efficient at removing mixed pollutants and reducing CO₂ than individual plants.

Development of a Tuneable NDIR Optical Electronic Nose

Electronic nose (E-nose) technology provides an easy and inexpensive way to analyse chemical samples. In recent years, there has been increasing demand for E-noses in applications such as food safety, environmental monitoring and medical diagnostics. Currently, the majority of E-noses utilise an array of metal oxide (MOX) or conducting polymer (CP) gas sensors. However, these sensing technologies can suffer from sensor drift, poor repeatability and temperature and humidity effects. Optical gas sensors have the potential to overcome these issues. This paper reports on the development of an optical non-dispersive infrared (NDIR) E-nose, which consists of an array of four tuneable detectors, able to scan a range of wavelengths (3.1–10.5 μm). The functionality of the device was demonstrated in a series of experiments, involving gas rig tests for individual chemicals (CO₂ and CH₄), at different concentrations, and discriminating between chemical standards and complex mixtures. The optical gas sensor responses were shown to be linear to polynomial for different concentrations of CO₂ and CH₄. Good discrimination was achieved between sample groups. Optical E-nose technology therefore demonstrates significant potential as a portable and low-cost solution for a number of E-nose applications.

Doped SnO2 Nanomaterials for E-Nose Based Electrochemical Sensing of Biomarkers of Lung Cancer

Lung cancer detection includes detection of a pattern formed by multiple volatile organic compounds. An individual material has limited selectivity and hence requires tailoring to improve the selectivity and sensing properties. An electronic nose (e-nose) is a concept/device that can help in achieving selectivity and specificity for multiple volatile organic compounds at the same time by using an array of sensors. In this paper, Co and Ni doping in tin oxide was used to investigate as a sensor material for e-nose development. These were synthesized using a sol-gel method and were characterized for structural, morphological, and elemental assessment using X-ray diffraction, field emission scanning electron microscopy, and Fourier transform infrared spectroscopy, which indicated the formation of the composite nanomaterial of SnO₂. These synthesized materials were then used as a working electrode in the form of a screen-printed electrode to determine 1-propanol and isopropyl alcohol (IPA) sensing characteristics. Electrochemical characterization was done by cyclic voltammetry (CV) and electrochemical impedance spectroscopy. In the case of CV studies, well-defined and distinct redox peaks are observed at different potential values indicating the changes due to the dopants. Ni doping in SnO₂ shows the highest sensitivity of 2.99 μA/ppb for isopropyl alcohol and 3.11 for 1-propanol, within the detection range. Furthermore, Co-SnO₂ shows selectivity for IPA, while Ni-SnO₂ is selective to 1-propanol against all other volatile compounds analyzed.

Tuning the Structure and Performance of Bulk and Porous Vapor Sensors Based on Co-continuous Carbon Nanotube-Filled Blends of Poly(vinylidene fluoride) and Polycarbonates by Varying Melt Viscosity

This work describes a new concept of porous vapor sensor materials based on co-continuous polycarbonate/poly(vinylidene fluoride)/multiwalled carbon nanotube (PC/PVDF/MWCNT) blend composites. The blend composites were fabricated by melt mixing in a one-step mixing process, and the MWCNT containing component (here PC) was extracted, leaving a MWCNT network on the continuous surface of the remaining component (here PVDF). First, by selecting three PCs with different molecular weights, the blend viscosity ratio and blend fineness and interfacial area were varied. At the chosen blend composition of 40/60 wt %, the desired co-continuous structure was achieved with MWCNTs selectively localized in PC. The conductive polymer composites (CPCs) with low-viscosity PC had the highest conductivity due to a combination of the best MWCNT dispersion and the coarsest blend morphology. The vapor sensing of CPC sensor materials with 1 wt % MWCNT was tested using saturated vapors of dichloromethane, acetone, tetrahydrofuran, and ethyl acetate, showing good interaction with PC. The compact compression molded CPC materials with low-viscosity PC showed the lowest relative resistance changes (R_rel) during the cyclic sensing tests, but a better recovery compared to corresponding CPCs with medium and high viscosity PC. The porous CPC sensors showed remarkable vapor sensing performance compared to the corresponding compact sensors with better sensing stability, reproducibility, and reversibility. Scanning electron microscopy (SEM) confirmed that a fraction of the nanotubes remained on the surface of the continuous, nonsoluble PVDF after PC extraction. The porous sensor material from which the low-viscosity PC was extracted showed the highest R_rel (e.g., around 1300% after 100 s immersion in acetone vapor) compared to all other organic vapors investigated. The difference in vapor measurement between compact and porous sensor materials was attributed to the different sensing mechanisms of polymer swelling for the compact and vapor absorption on the free CNT networks for the porous samples.

The Applications of Carbon Nanotubes in the Diagnosis and Treatment of Lung Cancer: A Critical Review

The importance of timely diagnosis and the complete treatment of lung cancer for many people with this deadly disease daily increases due to its high mortality. Diagnosis and treatment with helping the nanoparticles are useful, although they have reasonable harms. This article points out that the side effects of using carbon nanotube (CNT) in this disease treatment process such as inflammation, fibrosis, and carcinogenesis are very problematic. Toxicity can reduce to some extent using the techniques such as functionalizing to proper dimensions as a longer length, more width, and greater curvature. The targeted CNT sensors can be connected to various modified vapors. In this regard, with helping this method, screening makes non-invasive diagnosis possible. Researchers have also found that nanoparticles such as CNTs could be used as carriers to direct drug delivery, especially with chemotherapy drugs. Most of these carriers were multi-wall carbon nanotubes (MWCNT) used for cancerous cell targeting. The results of laboratory and animal researches in the field of diagnosis and treatment became very desirable and hopeful. The collection of researches summarized has highlighted the requirement for a detailed assessment which includes CNT dose, duration, method of induction, etc., to achieve the most controlled conditions for animal and human studies. In the discussion section, 4 contradictory issues are discussed which are invited researchers to do more research to get clearer results.

Development of a Sensing Array for Human Breath Analysis Based on SWCNT Layers Functionalized with Semiconductor Organic Molecules

A sensor array based on heterojunctions between semiconducting organic layers and single walled carbon nanotube (SWCNT) films is produced to explore applications in breathomics, the molecular analysis of exhaled breath. The array is exposed to gas/volatiles relevant to specific diseases (ammonia, ethanol, acetone, 2-propanol, sodium hypochlorite, benzene, hydrogen sulfide, and nitrogen dioxide). Then, to evaluate its capability to operate with real relevant biological samples the array is exposed to human breath exhaled from healthy subjects. Finally, to provide a proof of concept of its diagnostic potential, the array is exposed to exhaled breath samples collected from subjects with chronic obstructive pulmonary disease (COPD), an airway chronic inflammatory disease not yet investigated with CNT-based sensor arrays, and breathprints are compared with those obtained from of healthy subjects. Principal component analysis shows that the sensor array is able to detect various target gas/volatiles with a clear fingerprint on a 2D subspace, is suitable for breath profiling in exhaled human breath, and is able to distinguish subjects with COPD from healthy subjects based on their breathprints. This classification ability is further improved by selecting the most responsive sensors to nitrogen dioxide, a potential biomarker of COPD.

The potential of breath analysis to improve outcome for patients with lung cancer

Lung cancer remains the most common cause of cancer related death in both the UK and USA. Development of diagnostic approaches that have the ability to detect lung cancer early are a research priority with potential to improve survival. Analysis of exhaled breath metabolites, or volatile organic compounds (VOCs) is an area of considerable interest as it could fulfil such requirements. Numerous studies have shown that VOC profiles are different in the breath of patients with lung cancer compared to healthy individuals or those with non-malignant lung diseases. This review provides a scientific and clinical assessment of the potential value of a breath test in lung cancer. It discusses the current understanding of metabolic pathways that contribute to exhaled VOC production in lung cancer and reviews the research conducted to date. Finally, we highlight important areas for future research and discuss how a breath test could be incorporated into various clinical pathways.

Enhanced selectivity of target gas molecules through a minimal array of gas sensors based on nanoparticle-decorated SWCNTs

Layers of CNTs decorated with metal and metal–oxide nanoparticles can be used to develop highly selective gas sensor arrays.

Influence of Water Molecules on the Detection of Volatile Organic Compounds (VOC) Cancer Biomarkers by Nanocomposite Quantum Resistive Vapor Sensors vQRS

The anticipated diagnosis of various fatal diseases from the analysis of volatile organic compounds (VOC) biomarkers of the volatolome is the object of very dynamic research. Nanocomposite-based quantum resistive vapor sensors (vQRS) exhibit strong advantages in the detection of biomarkers, as they can operate at room temperature with low consumption and sub ppm (part per million) sensitivity. However, to meet this application they need to detect some ppm or less amounts of biomarkers in patients' breath, skin, or urine in complex blends of numerous VOC, most of the time hindered by a huge amount of water molecules. Therefore, it is crucial to analyze the effects of moisture on the chemo-resistive sensing behavior of carbon nanotubes based vQRS. We show that in the presence of water molecules, the sensors cannot detect the right amount of VOC molecules present in their environment. These perturbations of the detection mechanism are found to depend on the chemical interactions between water and other VOC molecules, but also on their competitive absorption on sensors receptive sites, located at the nanojunctions of the conductive architecture. This complex phenomenon studied with down to 12.5 ppm of acetone, ethanol, butanone, toluene, and cyclohexane mixed with 100 ppm of water was worth to investigate in the prospect of future developments of devices analysing real breath samples in which water can reach a concentration of 6%.

A chemiresistive sensor array from conductive polymer nanowires fabricated by nanoscale soft lithography

One-dimensional organic nanostructures are essential building blocks for high performance gas sensors. Constructing an e-nose type sensor array is the current golden standard in developing portable systems for the detection of gas mixtures. However, facile fabrication of nanoscale sensor arrays is still challenging due to the high cost of the conventional nanofabrication techniques. In this work, we fabricate a chemiresistive gas sensor array composed of well-ordered sub-100 nm wide conducting polymer nanowires using cost-effective nanoscale soft lithography. Poly(3,4-ethylene-dioxythiophene)-poly(styrene sulfonate) (PEDOT:PSS) nanowires functionalized with different self-assembled monolayers (SAMs) are capable of identifying volatile organic compounds (VOCs) at a low concentration range. The side chains and functional groups of the SAMs introduce different sensitivities and selectivities to the targeted analytes. The distinct response pattern of each chemical is subjected to pattern recognition protocols, which leads to a clear separation towards ten VOCs, including ketones, alcohols, alkanes, aromatics and amines. These results of the chemiresistive gas sensor array demonstrate that nanoscale soft lithography is a reliable approach for fabricating nanoscale devices and has the potential of mass producibility.

Discrimination between Alternative Herbal Medicines from Different Categories with the Electronic Nose

As alternative herbal medicine gains soar in popularity around the world, it is necessary to apply a fast and convenient means for classifying and evaluating herbal medicines. In this work, an electronic nose system with seven classification algorithms is used to discriminate between 12 categories of herbal medicines. The results show that these herbal medicines can be successfully classified, with support vector machine (SVM) and linear discriminant analysis (LDA) outperforming other algorithms in terms of accuracy. When principal component analysis (PCA) is used to lower the number of dimensions, the time cost for classification can be reduced while the data is visualized. Afterwards, conformal predictions based on 1NN (1-Nearest Neighbor) and 3NN (3-Nearest Neighbor) (CP-1NN and CP-3NN) are introduced. CP-1NN and CP-3NN provide additional, yet significant and reliable, information by giving the confidence and credibility associated with each prediction without sacrificing of accuracy. This research provides insight into the construction of a herbal medicine flavor library and gives methods and reference for future works.

Cross-Selectivity Enhancement of Poly(vinylidene fluoride-hexafluoropropylene)-Based Sensor Arrays for Detecting Acetone and Ethanol

Two methods for cross-selectivity enhancement of porous poly(vinylidene fluoride-hexafluoropropylene) (PVDF-HFP)/carbon black (CB) composite-based resistive sensors are provided. The sensors are tested with acetone and ethanol in the presence of humid air. Cross-selectivity is enhanced using two different methods to modify the basic response of the PVDF-HFP/CB sensing platform. In method I, the adsorption properties of PVDF-HFP/CB are altered by adding a polyethylene oxide (PEO) layer or by treating with infrared (IR). In method II, the effects of the interaction of acetone and ethanol are enhanced by adding diethylene carbonate (DEC) or PEO dispersed in DEC (PEO/DEC) to the film. The results suggest the approaches used in method I alter the composite ability to adsorb acetone and ethanol, while in method II, they alter the transduction characteristics of the composite. Using these approaches, sensor relative response to acetone was increased by 89% compared with the PVDF-HFP/CB untreated film, whereas sensor relative response to ethanol could be decreased by 57% or increased by 197%. Not only do these results demonstrate facile methods for increasing sensitivity of PVDF-HFP/CB film, used in parallel they demonstrate a roadmap for enhancing system cross-selectivity that can be applied to separate units on an array. Fabrication methods, experimental procedures and results are presented and discussed.

Sulfonated poly(ether ether ketone) [SPEEK] nanocomposites based on hybrid nanocarbons for the detection and discrimination of some lung cancer VOC biomarkers

The analysis of a volatolome is a promising approach to allow the early diagnosis of diseases such as cancers. However, one important challenge is to take the chemical fingerprint of the complex blend of volatiles, for many of them only present at the sub-ppm level. We have investigated a facile route to differentiate the chemo-resistive behaviour of quantum resistive vapour sensors (vQRS) and provide them with a strong methanol selectivity by simply changing the sulfonation degree of poly(ether ether ketone) up to 85%. The hybridization of carbon nanotubes (CNTs) with fullerene (C₆₀) structured in a 3D architecture by spray layer-by-layer (sLbL) has allowed us to boost significantly the sensitivity of sensors to reach the sub-ppm level (340 ppb). After their integration into an e-nose, PEEK-nanocarbon sensors were found to effectively discriminate both single and binary mixtures of volatile organic compounds (VOCs) and among all biomarkers to detect preferentially methanol with a high signal to noise ratio (200).

A disposable breath sensing tube with on-tube single-nanowire sensor array for on-site detection of exhaled breath biomarkers

This paper presents a novel disposable breath-sensing tube with an on-tube single-nanowire (NW) sensor array for noninvasive, simple, and on-site detection of exhaled breath biomarkers. Although various noninvasive detection methods for lung cancer biomarkers in breath samples exist, they are unsuitable for self-diagnostics and immediate detection because they entail complicated handling procedures and are time intensive. In this study, we simulated, fabricated, and characterized disposable nanosensors by using single TiO₂ and Ag NWs in flexible plastic tubes. The proposed sensors simultaneously detect 2-propyl-1-pentanol (2-PP) lung cancer biomarkers and changes in the humidity, flow rate, and temperature of the flowing gas. The optimal dimension of the tubes was determined and verified through dynamic simulations and experiments. The current tube design decreases sensing variation and moisture interference by 43.28% and 78.77%, respectively, compared with previous designs. In the future, the proposed breath sensor, which has a response time of less than 10 s, can be used in a tube for simple and quick screening of lung cancer patients with the 2-PP concentration exceeding 100 ppb. The developed breath-sensing platform can be applied in noninvasive on-site disease screening.

Valid Probabilistic Predictions for Ginseng with Venn Machines Using Electronic Nose

In the application of electronic noses (E-noses), probabilistic prediction is a good way to estimate how confident we are about our prediction. In this work, a homemade E-nose system embedded with 16 metal-oxide semi-conductive gas sensors was used to discriminate nine kinds of ginsengs of different species or production places. A flexible machine learning framework, Venn machine (VM) was introduced to make probabilistic predictions for each prediction. Three Venn predictors were developed based on three classical probabilistic prediction methods (Platt's method, Softmax regression and Naive Bayes). Three Venn predictors and three classical probabilistic prediction methods were compared in aspect of classification rate and especially the validity of estimated probability. A best classification rate of 88.57% was achieved with Platt's method in offline mode, and the classification rate of VM-SVM (Venn machine based on Support Vector Machine) was 86.35%, just 2.22% lower. The validity of Venn predictors performed better than that of corresponding classical probabilistic prediction methods. The validity of VM-SVM was superior to the other methods. The results demonstrated that Venn machine is a flexible tool to make precise and valid probabilistic prediction in the application of E-nose, and VM-SVM achieved the best performance for the probabilistic prediction of ginseng samples.

Love Acoustic Wave-Based Devices and Molecularly-Imprinted Polymers as Versatile Sensors for Electronic Nose or Tongue for Cancer Monitoring

Cancer is a leading cause of death worldwide and actual analytical techniques are restrictive in detecting it. Thus, there is still a challenge, as well as a need, for the development of quantitative non-invasive tools for the diagnosis of cancers and the follow-up care of patients. We introduce first the overall interest of electronic nose or tongue for such application of microsensors arrays with data processing in complex media, either gas (e.g., Volatile Organic Compounds or VOCs as biomarkers in breath) or liquid (e.g., modified nucleosides as urinary biomarkers). Then this is illustrated with a versatile acoustic wave transducer, functionalized with molecularly-imprinted polymers (MIP) synthesized for adenosine-5'-monophosphate (AMP) as a model for nucleosides. The device including the thin film coating is described, then static measurements with scanning electron microscopy (SEM) and electrical characterization after each step of the sensitive MIP process (deposit, removal of AMP template, capture of AMP target) demonstrate the thin film functionality. Dynamic measurements with a microfluidic setup and four targets are presented afterwards. They show a sensitivity of 5 Hz·ppm(-1) of the non-optimized microsensor for AMP detection, with a specificity of three times compared to PMPA, and almost nil sensitivity to 3'AMP and CMP, in accordance with previously published results on bulk MIP.

Biosensor-based detection of tuberculosis

Tuberculosis (TB), caused byMycobacterium tuberculosis(M.tb.), is one of the most prevalent and serious infectious diseases worldwide with an estimated annual global mortality of 1.4 million in 2010.

Optimal Sensor Selection for Classifying a Set of Ginsengs Using Metal-Oxide Sensors

The sensor selection problem was investigated for the application of classification of a set of ginsengs using a metal-oxide sensor-based homemade electronic nose with linear discriminant analysis. Samples (315) were measured for nine kinds of ginsengs using 12 sensors. We investigated the classification performances of combinations of 12 sensors for the overall discrimination of combinations of nine ginsengs. The minimum numbers of sensors for discriminating each sample set to obtain an optimal classification performance were defined. The relation of the minimum numbers of sensors with number of samples in the sample set was revealed. The results showed that as the number of samples increased, the average minimum number of sensors increased, while the increment decreased gradually and the average optimal classification rate decreased gradually. Moreover, a new approach of sensor selection was proposed to estimate and compare the effective information capacity of each sensor.

Hybrid Films of Graphene and Carbon Nanotubes for High Performance Chemical and Temperature Sensing Applications

A hybrid composite material of graphene and carbon nanotube (CNT) for high performance chemical and temperature sensors is reported. Integration of 1D and 2D carbon materials into hybrid carbon composites is achieved by coupling graphene and CNT through poly(ionic liquid) (PIL) mediated-hybridization. The resulting CNT/PIL/graphene hybrid materials are explored as active materials in chemical and temperature sensors. For chemical sensing application, the hybrid composite is integrated into a chemo-resistive sensor to detect a general class of volatile organic compounds. Compared with the graphene-only devices, the hybrid film device showed an improved performance with high sensitivity at ppm level, low detection limit, and fast signal response/recovery. To further demonstrate the potential of the hybrid films, a temperature sensor is fabricated. The CNT/PIL/graphene hybrid materials are highly responsive to small temperature gradient with fast response, high sensitivity, and stability, which may offer a new platform for the thermoelectric temperature sensors.

Breath sensors for lung cancer diagnosis

The scope of the applications of breath sensors is abundant in disease diagnosis. Lung cancer diagnosis is a well-fitting health-related application of this technology, which is of utmost importance in the health sector, because lung cancer has the highest death rate among all cancer types, and it brings a high yearly global burden. The aim of this review is first to provide a rational basis for the development of breath sensors for lung cancer diagnostics from a historical perspective, which will facilitate the transfer of the idea into the rapidly evolving sensors field. Following examples with diagnostic applications include colorimetric, composite, carbon nanotube, gold nanoparticle-based, and surface acoustic wave sensor arrays. These select sensor applications are widened by the state-of-the-art developments in the sensors field. Coping with sampling sourced artifacts and cancer staging are among the debated topics, along with the other concerns like proteomics approaches and biomimetic media utilization, feature selection for data classification, and commercialization.

Ultrasensitive QRS made by supramolecular assembly of functionalized cyclodextrins and graphene for the detection of lung cancer VOC biomarkers

A novel electronic nose system comprising functionalized β-cyclodextrin wrapped reduced graphene oxide (RGO) sensors with distinct ability of discrimination of a set of volatile organic compounds has been developed. Non-covalent modification of chemically functionalized cyclodextrin with RGO is carried out by using pyrene adamantane as a linker wherever necessary, in order to construct a supramolecular assembly. The chemical functionality on cyclodextrin is varied utilising the principle of selective chemical modification of cyclodextrin. In the present study, the combined benefits of the host-guest inclusion complex formation ability and tunable chemical functionality of cyclodextrin, as well as the high surface area and electrical conductivity of graphene, are utilized for the development of a set of highly selective quantum resistive chemical vapour sensors (QRS), which can be assembled in an electronic nose.

Silver nanoprism arrays coupled to functional hybrid films for localized surface plasmon resonance-based detection of aromatic hydrocarbons

We report the achievement of sensitive gas detection using periodic silver nanoprisms fabricated by a simple and low-cost lithographic technique. The presence of sharp tips combined with the periodic arrangement of the nanoprisms allowed the excitement of isolated and interacting localized surface plasmon resonances. Specific sensing capabilities with respect to aromatic hydrocarbons were achieved when the metal nanoprism arrays were coupled in the near field with functional hybrid films, providing a real-time, label-free, and reversible methodology. Ultra-high-vacuum temperature-programmed desorption measurements demonstrated an interaction energy between the sensitive film and analytes in the range of 55-71 kJ/mol. The far-field optical properties and the detection sensitivity of the sensors, modeled using a finite element method, were correlated to experimental data from gas sensing tests. An absorbance variation of 1.2% could be observed and associated with a theoretical increase in the functional film refractive index of ∼0.001, as a consequence to the interaction with 30 ppm xylene. The possibility of detecting such a small variation in the refractive index suggests the highly promising sensing capabilities of the presented technique.

Current breathomics--a review on data pre-processing techniques and machine learning in metabolomics breath analysis

We define breathomics as the metabolomics study of exhaled air. It is a strongly emerging metabolomics research field that mainly focuses on health-related volatile organic compounds (VOCs). Since the amount of these compounds varies with health status, breathomics holds great promise to deliver non-invasive diagnostic tools. Thus, the main aim of breathomics is to find patterns of VOCs related to abnormal (for instance inflammatory) metabolic processes occurring in the human body. Recently, analytical methods for measuring VOCs in exhaled air with high resolution and high throughput have been extensively developed. Yet, the application of machine learning methods for fingerprinting VOC profiles in the breathomics is still in its infancy. Therefore, in this paper, we describe the current state of the art in data pre-processing and multivariate analysis of breathomics data. We start with the detailed pre-processing pipelines for breathomics data obtained from gas-chromatography mass spectrometry and an ion-mobility spectrometer coupled to multi-capillary columns. The outcome of data pre-processing is a matrix containing the relative abundances of a set of VOCs for a group of patients under different conditions (e.g. disease stage, treatment). Independently of the utilized analytical method, the most important question, 'which VOCs are discriminatory?', remains the same. Answers can be given by several modern machine learning techniques (multivariate statistics) and, therefore, are the focus of this paper. We demonstrate the advantages as well the drawbacks of such techniques. We aim to help the community to understand how to profit from a particular method. In parallel, we hope to make the community aware of the existing data fusion methods, as yet unresearched in breathomics.